release-notes
The release-notes skill transforms technical documentation like JIRA tickets, PRDs, or changelogs into polished, user-facing release notes. It extracts changes, categorizes them as new features, improvements, fixes, or breaking changes, and rewrites each entry to emphasize user benefits in plain language rather than technical jargon. Use this skill when preparing product announcements, writing changelogs, or communicating shipping updates to end users across different product types.
git clone --depth 1 https://github.com/phuryn/pm-skills /tmp/release-notes && cp -r /tmp/release-notes/pm-execution/skills/release-notes ~/.claude/skills/release-notesSKILL.md
## Release Notes Generator Transform technical tickets, PRDs, or internal changelogs into polished, user-facing release notes. ### Context You are writing release notes for **$ARGUMENTS**. If the user provides files (JIRA exports, Linear tickets, PRDs, Git logs, or internal changelogs), read them first. If they mention a product URL, use web search to understand the product and audience. ### Instructions 1. **Gather raw material**: Read all provided tickets, changelogs, or descriptions. Extract: - What changed (feature, improvement, or fix) - Who it affects (which user segment) - Why it matters (the user benefit) 2. **Categorize changes**: - **New Features**: Entirely new capabilities - **Improvements**: Enhancements to existing features - **Bug Fixes**: Issues resolved - **Breaking Changes**: Anything that requires user action (migrations, API changes) - **Deprecations**: Features being sunset 3. **Write each entry** following these principles: - Lead with the user benefit, not the technical change - Use plain language — avoid jargon, internal codenames, or ticket numbers - Keep each entry to 1-3 sentences - Include visuals or screenshots if the user provides them **Example transformations**: - Technical: "Implemented Redis caching layer for dashboard API endpoints" - User-facing: "Dashboards now load up to 3× faster, so you spend less time waiting and more time analyzing." - Technical: "Fixed race condition in concurrent checkout flow" - User-facing: "Fixed an issue where some orders could fail during high-traffic periods." 4. **Structure the release notes**: ``` # [Product Name] — [Version / Date] ## New Features - **[Feature name]**: [1-2 sentence description of what it does and why it matters] ## Improvements - **[Area]**: [What got better and how it helps] ## Bug Fixes - Fixed [issue description in user terms] ## Breaking Changes (if any) - **Action required**: [What users need to do] ``` 5. **Adjust tone** to match the product's voice — professional for B2B, friendly for consumer, developer-focused for APIs. Save as a markdown document. If the user wants HTML or another format, convert accordingly.
The method for finding the gap between what a system is supposed to do and what the code actually does — the class of bug generic scanners miss because they have no model of intent. Defines what counts as documented intent, what counts as implementation evidence, which mismatches matter, and how to avoid hand-wavy findings. Use when auditing AI-built code, reviewing access control against documented permissions, or checking whether a codebase matches its own documentation.
The durable documentation set that makes an AI-built (vibe-coded) app reviewable before shipping. A small core every app needs — architecture, user/permission flows, permissions, variables/secrets, and a test-coverage map — plus conditional docs added only when they apply: emails, scheduled work, SEO, and embedded agents/automation. Defines what each doc must capture and how a reviewer or auditor uses it. Use when documenting a codebase for handoff, mapping user journeys and trust-boundary crossings, planning test coverage, or preparing for a security or performance audit.
Analyze A/B test results with statistical significance, sample size validation, confidence intervals, and ship/extend/stop recommendations. Use when evaluating experiment results, checking if a test reached significance, interpreting split test data, or deciding whether to ship a variant.
Perform cohort analysis on user engagement data — retention curves, feature adoption trends, and segment-level insights. Use when analyzing user retention by cohort, studying feature adoption over time, investigating churn patterns, or identifying engagement trends.
Generate SQL queries from natural language descriptions. Supports BigQuery, PostgreSQL, MySQL, and other dialects. Reads database schemas from uploaded diagrams or documentation. Use when writing SQL, building data reports, exploring databases, or translating business questions into queries.
Brainstorm team-level OKRs aligned with company objectives — qualitative objectives with measurable key results. Use when setting quarterly OKRs, aligning team goals with company strategy, drafting objectives, or learning how to write effective OKRs.
Create a Product Requirements Document using a comprehensive 8-section template covering problem, objectives, segments, value propositions, solution, and release planning. Use when writing a PRD, documenting product requirements, preparing a feature spec, or reviewing an existing PRD.
Generate realistic dummy datasets for testing with customizable columns, constraints, and output formats (CSV, JSON, SQL, Python script). Use when creating test data, building mock datasets, or generating sample data for development and demos.